Flow prediction in the lower Yellow River based on CEEMDAN-BILSTM coupled model

• Published in: Water science & technology. Water supply Vol. 23; no. 1; pp. 396 - 409
• Main Authors: Zhang, Xianqi, Qiao, Wenbao, Huang, Jiafeng, Shi, Jingwen, Zhang, Minghui
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.01.2023
• Subjects: Accuracy; Artificial intelligence; ceemdan-bilstm; Coefficients; flow prediction; Genetic algorithms; Hydrology; Methods; middle and lower reaches of the yellow river; neural network; Neural networks; Predictions; Rain; River flow; Rivers; Runoff; Stream flow; Time series; Water resources; Yellow River
• ISSN: 1606-9749; 1607-0798
• DOI: 10.2166/ws.2022.426

Abstract As one of the important hydrological elements of rivers, flow is of great significance to the development and utilization of water resources and the ecological environment. Based on the excellent nonlinear processing capability of CEEMDAN and the advantages of BILSTM in time-series data modeling, a coupled CEEMDAN-BILSTM model is constructed for flow prediction, and the i-month flows from 1951 to 2016 are used to predict the i-month flows from 2017 to 2021. The results show that the CEEMDAN-BILSTM coupled model predicts the trend more closely with the actual data variation, and the minimum relative error is 0.56 and maximum 9.48, which are maintained within 10%, and the deterministic coefficients are all greater than 0.9, so the prediction accuracy is high. The flow in month i of 5 years was picked up by monthly predictions for 66 consecutive years, which provides a new way of thinking about the prediction of river flow.